O poder preditivo dos agrupamentos funcionais fitoplanctônicos responde ao tipo de ambiente.

The search for more parsimonious predictors of biodiversity to simplify taxonomic approaches has been constant in ecology. The use of biodiversity measures based on functional characteristics of the species is a strategy widely used in environmental management and monitoring. Several studies have used functional groups to explain the dynamics of the phytoplankton community. We compared the predictability of the phytoplankton community from lakes, reservoirs and rivers, at the species level, taxonomic groups and four phytoplankton functional classification (Reynolds Functional Groups – RFGF, Reynolds et al., 2002; Morpho-Functional Groups – MFGF, Salmaso and Padisák, 2007; Morphological Based Functional Groups – MBFGF, Kruk et al., 2010; Geometrical Forms – GF, Stanca et al., 2013). We tested the hypotheses that (i) the predictability of the functional classifications depends on the type of environment assessed, (ii) the functional classifications with the highest number of groups will be more efficient predictors, (iii) the taxonomic approach have less predictive power than functional groups. We sampled 120 environments in dry period, between 1997 and 2015, including lakes, rivers and reservoirs, distributed in tropical and subtropical regions. As we expected, we registered higher predictability at the functional group level than at the species level, with the greatest predictability of functional groups in the lakes. The MBFGF showed better response than the others functional classifications probably because it is more suitable when used for large spatial scales, and because it considers different traits besides the shape of the species, as verified in GF. The high predictive power verified for the taxonomic classes indicates that general characteristics of high taxonomic levels can be used to explain the phytoplankton dynamics in different types of environments. Our study demonstrates that using the characteristics of the species is a better proxy than the species level to understand the ecological processes driving the assembly of the phytoplankton community, and it also could help to understand the relationship between the biodiversity and the ecosystem functioning.

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Bibliographic Details
Main Author: Zanco, Barbara Furrigo
Format: Thesis/Dissertation biblioteca
Language:Portuguese
Published: Universidade Estadual de Maringá. Departamento de Biologia. Programa de Pós-Graduação em Ecologia de Ambientes Aquáticos Continentais 2017
Subjects:Rios, Lagos de inundação, Reservatórios, Funcional, Rivers, Floodplain lakes, Reservoirs, Functional, Fitoplâncton de água doce, Comunidades, Ecologia de, Abordagem funcional, ASFA_2015::F::Freshwater ecology, ASFA_2015::F::Freshwater lagoons, ASFA_2015::F::Freshwater lakes, ASFA_2015::P::Phytoplankton, ASFA_2015::R::Reservoir dynamics, ASFA_2015::L::Lake ecology, ASFA_2015::S::Stream ecology,
Online Access:http://hdl.handle.net/1834/9869
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id dig-aquadocs-1834-9869
record_format koha
institution UNESCO
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-aquadocs
tag biblioteca
region Europa del Oeste
libraryname Repositorio AQUADOCS
language Portuguese
topic Rios
Lagos de inundação
Reservatórios
Funcional
Rivers
Floodplain lakes
Reservoirs
Functional
Fitoplâncton de água doce
Comunidades, Ecologia de
Abordagem funcional
ASFA_2015::F::Freshwater ecology
ASFA_2015::F::Freshwater lagoons
ASFA_2015::F::Freshwater lakes
ASFA_2015::P::Phytoplankton
ASFA_2015::R::Reservoir dynamics
ASFA_2015::L::Lake ecology
ASFA_2015::S::Stream ecology
Rios
Lagos de inundação
Reservatórios
Funcional
Rivers
Floodplain lakes
Reservoirs
Functional
Fitoplâncton de água doce
Comunidades, Ecologia de
Abordagem funcional
ASFA_2015::F::Freshwater ecology
ASFA_2015::F::Freshwater lagoons
ASFA_2015::F::Freshwater lakes
ASFA_2015::P::Phytoplankton
ASFA_2015::R::Reservoir dynamics
ASFA_2015::L::Lake ecology
ASFA_2015::S::Stream ecology
spellingShingle Rios
Lagos de inundação
Reservatórios
Funcional
Rivers
Floodplain lakes
Reservoirs
Functional
Fitoplâncton de água doce
Comunidades, Ecologia de
Abordagem funcional
ASFA_2015::F::Freshwater ecology
ASFA_2015::F::Freshwater lagoons
ASFA_2015::F::Freshwater lakes
ASFA_2015::P::Phytoplankton
ASFA_2015::R::Reservoir dynamics
ASFA_2015::L::Lake ecology
ASFA_2015::S::Stream ecology
Rios
Lagos de inundação
Reservatórios
Funcional
Rivers
Floodplain lakes
Reservoirs
Functional
Fitoplâncton de água doce
Comunidades, Ecologia de
Abordagem funcional
ASFA_2015::F::Freshwater ecology
ASFA_2015::F::Freshwater lagoons
ASFA_2015::F::Freshwater lakes
ASFA_2015::P::Phytoplankton
ASFA_2015::R::Reservoir dynamics
ASFA_2015::L::Lake ecology
ASFA_2015::S::Stream ecology
Zanco, Barbara Furrigo
O poder preditivo dos agrupamentos funcionais fitoplanctônicos responde ao tipo de ambiente.
description The search for more parsimonious predictors of biodiversity to simplify taxonomic approaches has been constant in ecology. The use of biodiversity measures based on functional characteristics of the species is a strategy widely used in environmental management and monitoring. Several studies have used functional groups to explain the dynamics of the phytoplankton community. We compared the predictability of the phytoplankton community from lakes, reservoirs and rivers, at the species level, taxonomic groups and four phytoplankton functional classification (Reynolds Functional Groups – RFGF, Reynolds et al., 2002; Morpho-Functional Groups – MFGF, Salmaso and Padisák, 2007; Morphological Based Functional Groups – MBFGF, Kruk et al., 2010; Geometrical Forms – GF, Stanca et al., 2013). We tested the hypotheses that (i) the predictability of the functional classifications depends on the type of environment assessed, (ii) the functional classifications with the highest number of groups will be more efficient predictors, (iii) the taxonomic approach have less predictive power than functional groups. We sampled 120 environments in dry period, between 1997 and 2015, including lakes, rivers and reservoirs, distributed in tropical and subtropical regions. As we expected, we registered higher predictability at the functional group level than at the species level, with the greatest predictability of functional groups in the lakes. The MBFGF showed better response than the others functional classifications probably because it is more suitable when used for large spatial scales, and because it considers different traits besides the shape of the species, as verified in GF. The high predictive power verified for the taxonomic classes indicates that general characteristics of high taxonomic levels can be used to explain the phytoplankton dynamics in different types of environments. Our study demonstrates that using the characteristics of the species is a better proxy than the species level to understand the ecological processes driving the assembly of the phytoplankton community, and it also could help to understand the relationship between the biodiversity and the ecosystem functioning.
format Thesis/Dissertation
topic_facet Rios
Lagos de inundação
Reservatórios
Funcional
Rivers
Floodplain lakes
Reservoirs
Functional
Fitoplâncton de água doce
Comunidades, Ecologia de
Abordagem funcional
ASFA_2015::F::Freshwater ecology
ASFA_2015::F::Freshwater lagoons
ASFA_2015::F::Freshwater lakes
ASFA_2015::P::Phytoplankton
ASFA_2015::R::Reservoir dynamics
ASFA_2015::L::Lake ecology
ASFA_2015::S::Stream ecology
author Zanco, Barbara Furrigo
author_facet Zanco, Barbara Furrigo
author_sort Zanco, Barbara Furrigo
title O poder preditivo dos agrupamentos funcionais fitoplanctônicos responde ao tipo de ambiente.
title_short O poder preditivo dos agrupamentos funcionais fitoplanctônicos responde ao tipo de ambiente.
title_full O poder preditivo dos agrupamentos funcionais fitoplanctônicos responde ao tipo de ambiente.
title_fullStr O poder preditivo dos agrupamentos funcionais fitoplanctônicos responde ao tipo de ambiente.
title_full_unstemmed O poder preditivo dos agrupamentos funcionais fitoplanctônicos responde ao tipo de ambiente.
title_sort o poder preditivo dos agrupamentos funcionais fitoplanctônicos responde ao tipo de ambiente.
publisher Universidade Estadual de Maringá. Departamento de Biologia. Programa de Pós-Graduação em Ecologia de Ambientes Aquáticos Continentais
publishDate 2017
url http://hdl.handle.net/1834/9869
work_keys_str_mv AT zancobarbarafurrigo opoderpreditivodosagrupamentosfuncionaisfitoplanctonicosrespondeaotipodeambiente
AT zancobarbarafurrigo thepredictivepowerofphytoplanktonfunctionalgroupsrespondstothetypeofenvironment
_version_ 1756075514347388928
spelling dig-aquadocs-1834-98692022-05-27T21:41:03Z O poder preditivo dos agrupamentos funcionais fitoplanctônicos responde ao tipo de ambiente. The predictive power of phytoplankton functional groups responds to the type of environment. Zanco, Barbara Furrigo Rios Lagos de inundação Reservatórios Funcional Rivers Floodplain lakes Reservoirs Functional Fitoplâncton de água doce Comunidades, Ecologia de Abordagem funcional ASFA_2015::F::Freshwater ecology ASFA_2015::F::Freshwater lagoons ASFA_2015::F::Freshwater lakes ASFA_2015::P::Phytoplankton ASFA_2015::R::Reservoir dynamics ASFA_2015::L::Lake ecology ASFA_2015::S::Stream ecology The search for more parsimonious predictors of biodiversity to simplify taxonomic approaches has been constant in ecology. The use of biodiversity measures based on functional characteristics of the species is a strategy widely used in environmental management and monitoring. Several studies have used functional groups to explain the dynamics of the phytoplankton community. We compared the predictability of the phytoplankton community from lakes, reservoirs and rivers, at the species level, taxonomic groups and four phytoplankton functional classification (Reynolds Functional Groups – RFGF, Reynolds et al., 2002; Morpho-Functional Groups – MFGF, Salmaso and Padisák, 2007; Morphological Based Functional Groups – MBFGF, Kruk et al., 2010; Geometrical Forms – GF, Stanca et al., 2013). We tested the hypotheses that (i) the predictability of the functional classifications depends on the type of environment assessed, (ii) the functional classifications with the highest number of groups will be more efficient predictors, (iii) the taxonomic approach have less predictive power than functional groups. We sampled 120 environments in dry period, between 1997 and 2015, including lakes, rivers and reservoirs, distributed in tropical and subtropical regions. As we expected, we registered higher predictability at the functional group level than at the species level, with the greatest predictability of functional groups in the lakes. The MBFGF showed better response than the others functional classifications probably because it is more suitable when used for large spatial scales, and because it considers different traits besides the shape of the species, as verified in GF. The high predictive power verified for the taxonomic classes indicates that general characteristics of high taxonomic levels can be used to explain the phytoplankton dynamics in different types of environments. Our study demonstrates that using the characteristics of the species is a better proxy than the species level to understand the ecological processes driving the assembly of the phytoplankton community, and it also could help to understand the relationship between the biodiversity and the ecosystem functioning. A busca por preditores mais parcimoniosos da biodiversidade como uma forma de simplificar abordagens taxonômicas tem sido constante. O uso de mensuradores da biodiversidade a partir de características funcionais das espécies vem sendo uma estratégia amplamente utilizada para fins de manejo e monitoramento ambiental. Muitos estudos têm se utilizado de grupos funcionais para explicar a dinâmica da comunidade fitoplanctônica. Nós comparamos a preditibilidade da comunidade fitoplanctônica de lagos, reservatórios e rios ao nivel de espécie, de grupos taxonômicos e de quatro classificações funcionais (Grupos Funcionais de Reynolds – GFR, Reynolds et al., 2002; Grupos Morfo-Funcionais – GMF, Salmaso e Padisák, 2007; Grupos Funcionais Baseados em Morfologia – GFBM, Kruk et al., 2010 e Formas geométricas – FGF, Stanca et al., 2013). Testou-se as hipóteses (i) o poder preditivo dos agrupamentos funcionais varia de acordo com o ambiente avaliado, (ii) os agrupamentos com maior número de grupos, serão mais eficientes por refletirem maior número de processos ecológicos, e (iii) a abordagem taxonômica terá menor poder preditivo comparado com os agrupamentos funcionais. Foram amostrados 120 ambientes, no período seco, entre os anos de 1997 a 2015, incluindo lagos, rios e reservatórios de regiões tropicais e subtropicais. Como esperado, registramos maior poder preditivo ao nivel de grupos funcionais do que ao nivel de espécie, com maior preditibilidade nos lagos. O agrupamento GFBM mostrou melhor resposta, provavelmente por ter avaliado ampla escala espacial e por considerar diferentes traços, além da forma das espécies. O alto poder preditivo verificado para as classes taxonômicas indica que características gerais de grandes grupos podem ser utilizadas para explicar a dinâmica fitoplanctônica em variados tipos de ambientes. Os resultados demonstram que o uso das características das espécies é um melhor proxy do que a nível de espécies para entender processos ecológicos determinantes da comunidade fitoplanctônica, e também pode auxiliar no entendimento das relações entre a biodiversidade e o funcionamento do ecossistema. Masters 2017-09-01T12:13:27Z 2017-09-01T12:13:27Z 2017 Thesis/Dissertation http://hdl.handle.net/1834/9869 pt http://nou-rau.uem.br/nou-rau/document/?code=vtls000226663 http://repositorio.uem.br:8080/jspui/handle/1/4876 47pp. Brasil Universidade Estadual de Maringá. Departamento de Biologia. Programa de Pós-Graduação em Ecologia de Ambientes Aquáticos Continentais